1

FLUCTUATIONS IN GOVERNMENT SIZE IN THE OECD: 1973-2011

James Obben1

School of Economics and Finance

Massey University

New Zealand

ABSTRACT

In the wake of the 2008/09 financial crisis, the concurrence of a ‘large’ government size (GS)

and slow economic growth has become quite stark in many countries. The widespread

expectation for governments to scale back assumes public sector downsizing is uniformly

growth-enhancing for all countries. Empirical evidence, however, is mixed. The study revisits

the GS-growth issue by analysing a balanced panel dataset for 24 OECD countries that

includes the most recent data. It investigates four aspects of the issue where past studies have

considered only one or two: the impact of GS on growth; the existence of the inverted-U

shaped relation between GS and economic growth (the Barro-Armey-Rahn-Scully or BARS

curve) and the resultant optimal GS; the differential impacts of GS depending on the growth

rate using quantile regression; decomposition of the GS time series to determine whether the

shocks to it leave permanent or transitory effects on its level. GS here is government final

consumption expressed as a percentage of GDP. The linear model shows a significant

negative effect of GS on growth. The BARS curve was confirmed for 13 out of the 24

countries, and among these the period average of GS exceeds the optimal size. For those

countries, downsizing could be growth-enhancing but not necessarily so for the other

countries. The quantile regression results show that the impact of GS is positive and

insignificant at low rates of economic growth and continually decreases as growth increases

until at some rate it turns negative and progressively significant with the growth rate. The

Hodrick-Prescott decomposition indicates that in all countries, except Australia, the shocks to

GS leave predominantly permanent (rather than transitory) effects.

Key words: government size, quantile regression, Hodrick-Prescott technique, OECD.

JEL: E62, H5, O4, O5.

1. INTRODUCTION

World Bank data indicate that government final consumption expenditures as a percentage of

GDP (henceforth, government size) for the whole world fluctuated upward from 14% in

1961, peaked at 18.9% in 2009 and fell slightly to 18.5% in 2011. Correspondingly, the world

economic growth rate of 4.4% in 1961 fluctuated thereafter reaching a nadir of -2.2% in 2009

and rose to 2.7% in 2011. The correlation between government size and economic growth

rate over those years is -0.67 for the world and -0.72 for the OECD. In each of the 51 years,

the average government size in the OECD countries, the group of countries which accounts

for an average of 82% of world GDP annually, is bigger than the world average government

size and the gap has been widening steadily over the last eight years. And especially in the

1 Email: J.Obben@massey.ac.nz. Tel.: (+64) 6 356 9099. Fax: (+64) 6 350 5660.

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wake of the 2008/09 global financial crisis, the concurrence of a ‘large’ government size and

slow economic growth has become quite stark in many countries. The widespread

expectation for governments to scale back assumes public sector downsizing is uniformly

growth-enhancing for all countries. Empirical evidence, however, is mixed although an

overwhelming majority of the studies on government size and growth surveyed by Bergh and

Henrekson (2011) found significant negative growth effect from government size. The

problems of large government size are highlighted by the current onerously high debt levels,

slow growth and high unemployment observed in the US and the euro-zone.

Given these observations and the primacy of the OECD in the world economy, the study

revisits the government size-growth issue by analysing a balanced panel dataset for 24 OECD

countries that includes the most recent data. It investigates four aspects of the issue where

past studies have considered only one or two: the impact of government size on growth; the

existence of the hypothesised inverted-U shaped relation between government size and

economic growth (the Barro-Armey-Rahn-Scully or BARS curve) and the resultant growth-

maximising or ‘optimal’ government size; the differential impacts of government size

depending on the growth rate using quantile regression; decomposition of the government

size time series to determine whether the shocks to it leave permanent or transitory effects on

its level.

The twenty countries that founded the OECD in 1961 have, over time, been joined by

fourteen others as at the end of 2012. Available World Bank data on these countries start in

1961 and end in 2011 at the time of this study. To obtain the longest time series for the

largest number of countries based on the years of accession of member countries, the decision

was made to include the countries which have been members since 1973. That approach

yielded a sample of 24 countries with 39 years of data. The names and international iso-codes

of the countries are: Australia (AUS), Austria (AUT), Belgium (BEL), Canada (CAN),

Denmark (DNK), Finland (FIN), France (FRA), Germany (GER), Greece (GRC), Iceland

(ISL), Ireland (IRL), Italy (ITA), Japan (JPN), Luxembourg (LUX), Netherlands (NLD),

New Zealand (NZL), Norway (NOR), Portugal (PRT), Spain (ESP), Sweden (SWE),

Switzerland (CHE), Turkey (TUR), United Kingdom (GBR) and United States (USA).

Tests of the impact of government size on economic growth with panel regression models

suggested a strong negative impact and, although the BARS curve was confirmed for the

whole group, the evidence was weak. The group growth-maximising government size was

estimated to be 7.4%, much lower than the group period average of government size of 19%.

Individual country BARS curves were also estimated and the diversity noted. The quantile

regression models for the whole sample suggest that the impact of government size is positive

at low rates of growth, declines as the growth rate increases and eventually turns negative.

Employing the Hodrick-Prescott filtering technique, the government size time series of each

country was decomposed into its trend/permanent and cycle/transitory components. From the

estimated variances of the components, it was inferred that the changes in government size

for virtually all the countries are predominantly of the permanent nature. The rest of the paper

is structured as follows. Section 2 reviews the literature on government size and its effects on

the economy and the literature on the decomposition of time series. Section 3 deals with the

data and methodology. Section 4 presents and discusses the analytical results and Section 5

concludes the paper.

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2. LITERATURE

2.1 The Growth-Government Size Relationship

The main economic reason for the continuing concern with government size is the

preponderance of the negative correlation with economic growth found in empirical studies

(Bergh and Henrekson, 2011). Economic theory, however, is uncertain about the direction of

the impact of government size on the rate of economic growth; persuasive arguments can be

made for both positive and negative impacts of government size. Traditionally, one point of

view asserts that a larger government size can be a powerful engine for growth through the

development of a legal, administrative and economic infrastructure and by securing an

increase in productive investment and providing a socially optimal direction for growth and

development. The government also plays the crucial role of harmonising conflicts between

private and social interests and the prevention of exploitation of the country by foreigners.

This point of view may be said to use the theory of market failures to justify state

interventionism. An opposing point of view holds that as the public sector increases in size,

greater and greater taxation needs to be extracted to finance the expenditure. The increasing

taxation distorts economic incentives to work, save and invest and lowers the productivity of

the system. The economy slows down as a result. This point of view may be said to use the

theory of government failures to vilify large government size.

The relationship between government size and economic growth was at first studied in the

framework of a linear model of a Cobb-Douglas production function pioneered by Feder

(1982) but has since been investigated with a variety of analytical techniques. Mirroring the

different points of view, some of the studies have found positive correlation between

government size and economic growth (e.g., Ram, 1986; Kormendi and Meguire, 1986;

Grossman, 1990; Dar and AmirKhalkhali, 2002) whilst others have found negative

relationship between government size and economic growth (e.g., Cameron, 1982; Landau,

1985; Saunders, 1985, 1986; Guseh, 1997; Gwartney et al., 1998; Tanninen, 1999; Fölster

and Henrekson, 2001; Dar and AmirKhalkhali, 2002; Bergh and Henrekson, 2011; and

Afonso and Jalles, 2011). Unsurprisingly, other studies have reported mixed or insignificant

results (e.g., Conte and Darrat, 1988; Ghali, 1998; Grimes, 2003). Most empirical studies

have used the linear model; other studies, proferring that the conflicting results could be due

to a nonlinear relationship have investigated and found nonlinear impact of government size

on economic growth (e.g., Barro, 1990; Sheehey, 1993; Armey, 1995; Rahn and Fox, 1996;

Vedder and Galloway, 1998; Scully, 1998, 2003; and Chen and Lee, 2005). The underlying

argument is that a modicum of government is required to avoid anarchy and to foster

economic activity and growth up to a certain extent; beyond that a bloated government can

impede growth through inefficiency and diminishing marginal productivity. The resulting

inverted-U shape of the relationship is named the Barro-Armey-Rahn-Scully or BARS curve2

after these researchers who popularised the concept and made it a working tool of

contemporary economic analysis (Chobanov and Mladenova, 2009; Forte and Magazzino,

2010; Facchini and Melki, 2011). The peak of the BARS curve identifies the growth-

maximising size of government or the optimum government size. On why there have been

conflicting empirical results, the New Zealand Treasury (2011, pp. 9-10) notes that,

2 The ‘BARS curve’ is similar to the ‘Laffer curve’ (Laffer, 2004) in shape except that the latter shows the

relationship between tax revenue and tax rates and the former shows the relationship between economic growth

rate on the vertical axis and government size on the horizontal axis. The BARS curve has variously been

referred to as the Armey curve, the Rahn curve and the Scully curve.

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‘Though some types of expenditure enhance economic growth, at some point the

economic costs of raising taxes to fund that expenditure will outweigh its benefits.

This suggests that there is an optimal level of government expenditure, from an

economic growth perspective, which balances the economic benefits of expenditure

against the economic costs of taxes. However, the economic growth impacts will

depend on the tax and expenditure mix as well as the level of expenditure.’

The empirical studies on the government size-growth relationship usually estimate cross-

country growth regressions that attempt to take account of the range of determinants of

economic growth. The determinants include ‘state’ variables (physical and human capital and

labour) and ‘control’ variables such as the ratio of government consumption or total

expenditure to GDP, initial GDP, share of domestic investment in GDP, trade openness,

movements in the terms of trade, indicators of macroeconomic stability, the fertility rate and

institutional quality (Barro and Sala-i-Martin, 2003; Afonso and Jalles, 2011). From the

simple bivariate correlation between government size and growth reported by the pioneers

(e.g., Smith, 1975; Cameron, 1982; Saunders, 1985) the analytical techniques employed have

progressed through multivariate pooled cross-section OLS regression models (Barro 1991;

Barro and Sala-i-Martin, 1992), cointegration and vector error correction models (Ghali,

1998), panel fixed and random effects estimations (e.g., Fölster and Henrekson, 2001;

Romero-Avila and Strauch, 2008), random coefficients model (e.g., Dar and AmirKhalkhali,

2002), threshold regression methodology (Chen and Lee, 2005), smooth transition

autoregressive (STAR) framework (Chiou-Wei et al., 2010); quantile regression (e.g., Chen

et al., 2011), and polynomial analysis (Herath, 2012). The variation in the results could be

attributed to the differences in analytical methods, country groups, control variables,

indicators for government size and economic growth,3 data types and sample periods that

have been used. Other nontrivial methodological problems (such as endogeneity and data

issues) trouble all the studies.

A number of critical reviews of the empirical evidence have reported that alternative proxies

of government size have produced contradictory or insignificant results (Agell et al., 1997;

Temple, 1999; Myles, 2000; and Nijkamp and Poot, 2004). The summary of the empirical

evidence is perhaps best captured with the characterisation offered by Barro and Sala-i-

Martin (2003) that government size did have negative impact on economic growth in simple

models with a relatively small number of variables; however, it became insignificant when

additional variables were added to the model, suggesting that government size was capturing

the impact of these other variables in the simple models. The generality of this caveat is

exemplified by the finding by Afonso and Jalles that, in their relatively sophisticated panel

methodology, when the European Commission numerical fiscal rules are controlled for, the

previously very significant negative impact of government size for European Union member

countries fizzles into insignificance (2011, p. 20). In view of all of this, the New Zealand

Treasury 2025 Taskforce (2010) recommends that the results of cross-country regressions

should be read as illustrative rather than determinative.

3 For economic growth some studies used the growth rate of real GDP (e.g., Dar and AmarKhalkali, 2002; Chen

et al., 2011; Herath, 2012) and others used the growth rate of real GDP per capita (e.g., Chiou-Wei et al., 2010).

Proxies of government size that have been used include government total expenditure as a ratio of GDP (e.g.,

Dar and AmarKhalkali, 2002; Chen et al., 2011; Herath, 2012), government final consumption as a ratio of GDP

(e.g., Chiou-Wei et al., 2010), tax revenue as a ratio of GDP (e.g., Agell et al., 2006; Bergh and Henrekson,

2011).

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As noted earlier, in most of the studies, linear regression was used. Linear regression is a

conditional mean approach which means the estimated coefficients indicate how much the

expected value of the dependent variable (given the values of the independent variables) is

expected to change given a one unit change in the variables the coefficients represent, ceteris

paribus. In the growth models the coefficient of the government size variable reflects the

impact of government size on the growth rate of countries with growth rates similar to the

sample mean. The coefficient cannot reflect the growth impacts on countries with growth

rates lower or higher than the average growth rate. Those effects can be captured by using

quantile regression. Quantile regression shows the relation between a set of independent

variables and specific percentiles (quantiles) of the distribution of the dependent variable.

Therefore, a growth quantile regression parameter for government size estimates the growth

impact for a specified quantile of the growth rate. In this study, the growth-government size

relationship will be tested additionally with a quantile regression model. The extra insight

from quantile regression model is that it can reveal whether the effect of government size

depends on the percentile (quantile) of the distributions of the variables. It could be that the

growth impact of government size will be different depending on whether an economy is

growing slower or faster than average. Quantile regression will allow us to obtain a more

comprehensive picture. Chen et al. (2011) claim that the method had not been applied to the

growth-government size relationship prior to their study. This current study may be the

second one to apply the method to the issue using a different dataset.

2.2 Decomposition of Nonstationary Economic Time Series

Any nonstationary (trending) time series can be decomposed into permanent (or ‘trend’) and

transitory (or ‘cycle’) components (Garratt et al., 2006). The decomposition principle entails

applying some pre-filtering procedure, usually univariate in nature, to extract a trend (or

permanent component). The trend could be deterministic or stochastic. The deviations about

the trend constitute the detrended series or transitory/cycle component that is assumed to be

stationary. The trend component captures shocks that have a permanent effect on the level of

the variable, and the cycle component captures shocks that only have a temporary effect on

the level of the variable. There is no unique way to decompose a time series into trend and

cyclical parts. The main competing decomposition techniques are Beveridge and Nelson

(1981) model, the unobserved components models of Harvey (1985) and Clark (1987), the

business cycle filters of Hodrick and Prescott (1997) and Baxter and King (1999), and some

others such as wavelet transformation (Padda, 2011) and phase average trend (Boschan and

Ebanks, 1978; Zarnowitz and Ozyildirim, 2006). The decomposition techniques are

distinguished by how they define the trend and cycle components, and there are numerous

combinations of these. For instance, the Beveridge-Nelson and Harvey-Clark methods define

the trend as a random walk with drift but they differ in how they define serial correlation in

the model. Since the detrending filters used by the various techniques extract different kinds

of information from the data under consideration, the decomposition results vary and each

method suffers from significant deficiencies (Padda, 2011). The Hodrick-Prescott (HP)

method, however, has survived a lot of tests and is now perhaps the most popular.4 Hence the

HP decomposition method is adopted in this study. 4 Banerji and Dua (2011) report that the OECD has switched from phase average trend (PAT) method to the HP

as their decomposition method in growth cycle analysis. For its growth cycle, Statistics New Zealand employs

HP, Baxter-King and the Henderson filters (see Statistics New Zealand, 2007).

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3. METHODOLOGY AND DATA

3.1 The Analytical Methods

3.1.1 The Growth-Government Size Model

To check the growth-government size relationship in the data set, this study adopted the

growth equation expounded by Dar and AmirKhalkhali (2002) and which was utilised by

Chen et al. (2011). Extending the arguments in the Solow [exogenous] growth accounting

method which postulates that the rate of economic growth is a function of the growth in total

factor productivity (TFP) and the weighted growth rates of capital and labour, Dar and

AmirKhalkhali include government size and the growth rate of exports to endogenise the

growth model. That is,

gY = f(gK, gL, gEX, GS) (1)

where gY is growth rate of GDP, gK is growth rate of capital, gL is growth rate of the labour

force and GS is government size. Dar and AmirKhalkhali argue that ‘export growth (through

its favourable impact on the efficiency of resource use, innovative activity and the rate of

technical progress, and the realisation of economies of scale) raises TFP growth and, by

implication, economic growth.’ Government size, like export expansion, impacts on

economic growth through its impact on either total or individual factor productivity. In the

literature, some studies prefer to use growth in the per capita GDP to represent economic

growth in which case the per-capita equivalent of the growth model may be written as:

gy = f(gk, gEX, GS) (2)

where lower case y and k represent per capita GDP and capital per capita, respectively.

For the empirical estimation, panel regression techniques rather than pooled OLS were

employed because the latter is biased (from omitting a time-constant variable for the

countries) and is also inconsistent if the time-constant variable and the explanatory variables

are correlated (Wooldridge, 2013, pp. 484-496). The specific counterparts of equations (1)

and (2) to be estimated in this study can be written, respectively, as equations (3) and (4):

GRYit = β0 + β1GRKit + β2GRLit + β3GREXit + β4GOVSIZEit + ui + eit (3)

GRYPCit = γ0 + γ1GRKPCit + γ2GREXit + γ3GOVSIZEit + ui + eit (4)

for i = 1, …, 24; t = 1, …, 39, and where GRY is growth rate of GDP, GRK is the growth rate

of gross capital stock, GRL is the growth rate of the labour force, GREX is rate of export

expansion, GOVSIZE is government final consumption as percentage of GDP, GRYPC is the

growth rate of per capita GDP, GRKPC is the growth rate of capital per capita, ui is the

unobserved heterogeneity or the fixed effects for country i that is assumed to be correlated to

all explanatory variables at all times, and eit represents the idiosyncratic error or unobserved

factors that change over time and affect growth. To check for the existence of the BARS

curve (or nonlinear relationship) and potentially estimate an optimal value of government

size, the quadratic term for government size can be added to each of equations (3) and (4).

The main reason for implementing panel estimation is to allow for the unobserved effect ui to

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be correlated with the explanatory variables. That is, we want to allow the unmeasured

country-specific time-invariant factors that affect growth to also be correlated with

government size. The intercept reported in the panel model is the average across the countries

of the estimated country-specific intercepts. However, individual country intercepts can be

estimated with an appropriate formula and they show whether the unobserved fixed effects

that contribute to growth are above or below the average in the sample (Wooldridge, 2013, p.

489). The Hausman test would be used to choose between random effects and fixed effects.

Quantile regressions of equations (3) and (4) were also estimated to check whether the

regression parameters changed with quantiles of the distribution of the dependent variable.

The quantile regression methodology is described by Koenker (2005) and Koenker and

Bassett (1978).

3.1.2 The Hodrick-Prescott Decomposition Model

As mentioned in Section 2.2, the competing decomposition techniques are distinguished by

how they define the trend and cycle components. A brief description of the HP technique is

offered here. Let a time series yt be viewed as the sum of a trend component τt and a cyclical

component ct. That is,

yt = τt + ct for t =1,…., T. (5)

The HP method of extracting τt requires the minimization of the cost

∑ ( ) ∑ ( ) ( )

(6)

where the first term penalises the variance in the cyclical component while λ > 0 penalizes

variability in the trend component and therefore describes how much the lack of smoothness

in the trend contributes to the overall cost. The larger λ is, the smoother is the trend

component. For annual data, Hodrick and Prescott recommend a value of λ = 100.

3.2 The Data

All the data were sourced from the websites of the World Bank. Table 1 reports the averages

of the variables of interest for the sampled countries. Since the main focus of the study is on

government size, that variable is the one that will be described in some detail in this section.

The raw panel data on government size are presented in Appendix 1. The values of

GOVSIZE ranged from a minimum of 6.1% (posted by Turkey in 1988) to a maximum of

29.8% (posted by Denmark in 2009), averaging 18.95%. The country whose period average

comes closest to it is Austria, with 18.7%. The country with the highest sample average is

Sweden (26.9%) followed by Denmark (25.9%) and Netherlands (24.0%); and the country

with the lowest sample average is Turkey with 10.1%. New Zealand and Australia have

period averages of 18.3% and 17.7%, respectively. The year 2009 was when the sample

annual average peaked (at 21.6%) and the minimum yearly average occurred in 1973 (at

15.6%). Consistent with the upward trend of government size in the sample period, the range

of values at the start of the study period was from 8.6% to 23% and at the end of the study

period it was from 11.1% to 28.6%. In the sample, labour force, capital stock and capital per

worker grew at the average rates of 0.7%, 2.9% and 2.2% per annum, respectively. For the

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purposes of the quantile regressions, the summary statistics and the bar charts of the

distributions of GRYPC (growth rate of per capita GDP) and GRY (growth rate of aggregate

GDP) are presented in Figure 1. Both proxies of economic growth are slightly skewed to the

left. GRY ranged from -8.5% to 11.2% and averaged 2.6%; GRYPC ranged from -9.0% to

11.2% and averaged 1.9%.

To check the extent of co-movements of the government size variable between countries, the

cross-country correlations were estimated. Appendix 2 reports the pair-wise correlations of

government size between countries. Of the 276 calculations, about 81% are positive and 19%

negative, suggesting a high incidence of co-movement among the series. The highest

estimated correlation is 0.97 between Iceland and Portugal. Other very highly correlated

countries are France and Spain (0.93), Finland and Spain (0.92) and Iceland and Japan (0.90).

New Zealand is most highly correlated with Netherlands (0.75), second with Denmark (0.71)

and least with Iceland (0.16). The correlation with Australia is moderate at 0.51.

Table 1

Basic Average Statistics for the 24 OECD Countries (%)

Country GRY GRYPC GRK GRKPC GRL GREX GOVSIZE

AUS 3.15 1.79 4.71 3.23 1.48 4.90 17.68

AUT 2.38 2.09 2.16 1.79 0.36 5.33 18.72

BEL 2.14 1.81 2.54 2.07 0.47 4.22 22.01

CAN 2.83 1.67 4.28 3.07 1.21 4.05 21.01

DNK 1.80 1.51 2.34 2.16 0.19 4.86 25.85

FIN 2.59 2.20 2.61 2.18 0.43 5.38 21.13

FRA 2.14 1.59 1.99 1.39 0.60 4.75 22.54

GER 1.99 1.89 1.48 1.27 0.21 5.77 19.78

GRC 1.89 1.27 0.40 -0.13 0.52 5.56 16.22

ISL 3.17 2.09 4.06 3.07 0.99 4.47 21.08

IRL 4.37 3.31 2.96 1.90 1.06 9.03 18.55

ITA 1.99 1.70 1.93 1.60 0.33 4.36 18.61

JPN 2.48 2.03 1.35 0.98 0.36 5.96 15.54

LUX 3.78 2.75 4.89 3.72 1.18 5.79 15.81

NLD 2.39 1.82 2.05 1.35 0.71 5.03 23.96

NZL 2.32 1.35 3.37 2.21 1.16 3.96 18.30

NOR 2.95 2.36 2.88 2.21 0.66 3.83 20.40

PRT 2.59 2.08 2.94 2.55 0.39 4.99 16.28

ESP 2.63 1.88 2.48 1.64 0.84 6.09 16.34

SWE 2.19 1.81 2.58 2.16 0.42 5.03 26.90

CHE 2.18 1.58 1.17 0.58 0.59 4.21 10.85

TUR 4.28 2.52 8.16 6.40 1.76 9.25 10.09

GBR 2.19 1.90 2.73 2.47 0.26 4.28 20.61

USA 2.77 1.76 3.16 2.29 0.87 6.04 16.45

All 2.63 1.95 2.88 2.17 0.71 5.30 18.95

Sources: World Bank and OECD databases.

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Figure 1

Frequency Distributions and Summary Statistics of the Growth Variables

4. ANALYTICAL RESULTS

4.1 Impact of Government Size and the BARS Curve

Before decomposing the government size time series, the effect of government size on

economic growth in the dataset was checked. In connection with this, two models were

estimated. In the first one, the dependent variable is GRYPC – the growth rate of per capita

GDP (equation (6)); and in the second one, the dependent variable is GRY – the growth rate

of GDP (equation (5)). All variables were expressed in percentages. Each model was then

augmented with the quadratic term of the government size variable to check for the existence

of the BARS curve. In the growth panel regressions, the Hausman test rejected the random

effects in preference for the fixed effects. The fixed effects results are therefore reported in

Table 2.

The results of the linear models in Table 2 (Models 1.1 and 2.1) indicate that government size

is significantly negatively related to the growth rates of both GDP and per capita GDP. This

corroborates the results of the majority of empirical studies as mentioned in Section 2. An

increase of one percentage point in government size is expected to decrease the growth rates

of GDP and per capita GDP by about 0.13 and 0.11 percentage points, respectively.

Illarionov and Pivarova (2002) studying the OECD countries over the 1960-2000 period

estimated that a rise of one percentage point in the share of public expenditure on GDP came

with a reduction of 0.1 percentage point in the average growth rate of economic activity.

Other studies on the OECD/EU have reported similar findings.5 The capital, labour and

export variables take the expected positive signs in all the models and they are all very

significant.

5 For instance, Gwartney et al. (1998), Folster and Henrekson (2001), Pevcin (2004) and Afonso and Furceri

(2008).

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Table 2

Panel (Fixed Effects) Regression Results

Variable

Dependent Variable is GRYPC Dependent Variable is GRY

Model 1.1

(Linear)

Model 1.2

(Quadratic)

Model 2.1

(Linear)

Model 2.2

(Quadratic)

Constant 3.1591***

(4.96)

1.5565

(1.22)

3.9236***

(6.27)

3.5324***

(2.86)

GRKPC 0.1492***

(26.94)

0.1488***

(26.86)

GRK 0.1514***

(28.55)

0.1513***

(28.49)

GRL 0.1024***

(3.38)

0.1027***

(3.39)

GREX 0.1061***

(11.61)

0.1076***

(11.70)

0.1081***

(12.36)

0.1085***

(12.31)

GOVSIZE -0.1107***

(-3.33)

0.0738

(0.56)

-0.1252***

(-3.87)

-0.0803

(-0.64)

GOVSIZE_SQR -0.0050

(-1.44)

-0.0012

(-0.37)

Statistics

R2 0.7050 0.7057 0.7386 0.7387

Adj R2 0.6833 0.6837 0.7191 0.7188

No. of obsvns 936 936 936 936 Notes: Figures in parentheses are t-statistics. Triple, double and single asterisks (i.e., ***, ** and *)

indicate statistical significance at the 1%, 5% and 10% levels, respectively.

A BARS curve exists if the government size variable takes a positive sign and its quadratic

term takes a negative sign. The introduction of the quadratic term of government size points

to the existence of an inverted-U relationship (and therefore the existence of the BARS curve)

in the whole sample when growth rate of per capita GDP is used to represent economic

growth (Model 1.2) but not when economic growth is represented with GDP growth rate. It is

not clear why this is the case. It would seem from this result that for analytical purposes

economic growth is better captured with per capita GDP growth rate than with GDP growth

rate.6 Although the quadratic relationship is not statistically significant, it is estimated that the

optimal government size for the whole sample is 7.38%. Previous studies have confirmed the

existence of the BARS curve for various countries using growth rates of either GDP or per

capita GDP: Vedder and Galloway (1998) for the US, Canada, Denmark, Italy, Sweden and

Britain; Handoussa and Reiffers (2003) for Tunisia; Pevcin (2004) for 12 European countries;

Radwan and Reiffers (2004); Chen and Lee (2005) for Taiwan; Chiou-Wei et al. (2010) for

South Korea, Taiwan and Thailand; De Witte and Moesen (2010) for 23 OECD countries

using the nonparametric data envelopment analysis (DEA); and Herath (2012) for Sri Lanka.

6 Herath (2012) explains that real GDP is an aggregate figure that does not account for differing sizes of nations

and the better measurement of actual economic growth is per capita income because it reflects the average

standard of living of individual members of the population.

11

Compared with the group period average of 18.95%, the estimated optimal government size

of 7.38% shows that the typical OECD country exceeded the threshold level by more than

two-and-a-half times. Because some past studies used government total expenditure rather

than final consumption expenditure, the estimate of the optimal government size can only

truly be compared with those studies that utilised the share of government final consumption

in GDP to represent government size.7 In that regard, Chobanov and Mladenova (2009)

estimated an optimum government size of 10.4% based on 1961-2005 data for a panel of 81

countries. Owing to model and data limitations, the authors thought their estimate was

probably biased upwards. Davies (2008) studied the panel dataset on 154 countries over the

period 1975 through 2002 and estimated that the growth-maximising government size was

about 8.5%. Chiou-Wei et al. (2010), studying South Korea, Malaysia, Singapore, Taiwan

and Thailand and using the same indicators for government size and economic growth as this

current study, estimated that the threshold of government size for those countries (except

Malaysia) was about 11% although the individual country threshold levels were different.

The authors did not find nonlinearity in the Malaysian data. It must be remembered that the

data used by Chiou-Wei et al. (2010) covered the 1961-2004 period and the authors

implemented the STAR model.

Following the confirmation of the existence of the BARS curve in the consolidated data when

GDP per capita growth rate is the dependent variable, efforts were made to find out if the

curve existed for individual countries. The OLS results for the individual countries and the

estimated turning points of government size are reported in Table 3. It will be seen in Table 3

that the BARS curve (the inverse-U relationship) is confirmed for only 13 out of the 24

countries. For the rest of the countries, the relationship was estimated to be U-shaped, which

contradicts the BARS curve. However, there is a precedence of this in the literature, as it will

be disclosed shortly. Among the 13 countries for which the BARS curve was confirmed, the

estimated optimal government size ranged from 11.04% (for TUR) to 23.10% (for DNK).

Eleven of these countries (AUS, CAN, DNK, FIN, FRA, ISL, ITA, LUX, NZL, SWE and

GBR) had period averages larger than their estimated optimal government sizes. The inverse-

U relationship was strong in the cases of CAN, FIN, NZL and GBR but weak in the rest. For

these four countries (Canada, Finland, New Zealand and Great Britain), therefore,

downsizing could be growth-enhancing. The two countries that had period averages smaller

than the estimated optimal government sizes – Portugal (PRT) and Turkey (TUR) – could

presumably benefit from increases in government size. It is worthy of note that for four of the

five Asian countries in their sample, Chiou-Wei et al. (2010) estimated the turning point in

government size occurred at about 11% for Korea, Singapore and Thailand and about 16%

for Taiwan.8 For Singapore, however, the relationship was estimated to be U-shaped, in

contradiction to the BARS curve. The authors explained that, relative to the other countries,

Singapore’s government was strong and efficient enough to positively influence economic

growth even when it got bigger than 11%. In the current study, however, it is surmised that a

U-shaped relationship indicates that spending cuts would crimp growth rather than boost it.

7 Studies that utilised total government expenditure as ratio of GDP have typically reported optimal government

size ranging from 20% to 30% (Chobanov and Mladenova, 2009). 8 In an earlier study, Chen and Lee (2005) had estimated a threshold level of about 15% for Taiwan.

12

Table 3

Results of Regressions to Estimate the Optimal Government Size for the Sample Countries

Country Independent Variable

R2 Shape

Turning

Point

Period

Avge Constant GRKPC GREX GOVSIZE GOVSIZE2

AUS -23.367

(-1.07)

0.1976

(9.41)

0.1051

(3.11)

3.0134

(1.10)

-0.0935

(-1.10) 0.7504 ∩ 16.12% 17.7%

AUT 18.3662

(0.97)

0.1519

(4.83)

0.1058

(2.11)

-1.5027

(-0.70)

0.0314

(0.52) 0.6723 U 23.96% 18.7%

BEL 10.7938

(0.52)

0.1031

(3.01)

0.2048

(4.70)

-0.6825

(-0.35)

0.0102

(0.23) 0.8230 U 33.49% 22.0%

CAN -65.48

(-2.17)

0.1134

(4.96)

0.2005

(7.53)

6.7738

(2.35)

-0.1721

(-2.51) 0.8522 ∩ 19.68% 21.0%

DNK -19.772

(-1.46)

0.1839

(13.07)

0.0829

(3.08)

1.8062

(1.69)

-0.0391

(-1.85) 0.8672 ∩ 23.10% 25.9%

FIN -33.977

(-1.73)

0.1827

(8.19)

0.0965

(2.52)

3.8521

(2.06)

-0.1020

(-2.31) 0.8539 ∩ 18.88% 21.1%

FRA -0.2196

(-0.02)

0.1464

(8.78)

0.1134

(6.23)

0.5573

(0.62)

-0.0225

(-1.07) 0.9221 ∩ 12.39% 22.5%

GER 82.5186

(1.73)

0.1984

(11.61)

0.1743

(8.19)

-8.5865

(-1.77)

0.2243

(1.83) 0.8321 U 19.14% 19.8%

GRC 26.2715

(2.36)

0.2154

(7.30)

0.0795

(2.45)

-3.2662

(-2.42)

0.1032

(2.48) 0.6338 U 15.83% 16.2%

ISL -12.587

(-0.80)

0.1318

(8.06)

0.3633

(7.61)

1.4570

(0.98)

-0.0398

(-1.15) 0.7632 ∩ 18.31% 21.1%

IRL 24.9305

(1.20)

0.1096

(2.78)

0.3389

(5.77)

-2.4103

(-1.05)

0.0569

(0.91) 0.7243 U 21.17% 18.5%

ITA -8.6719

(-0.63)

0.2033

(16.00)

0.0961

(5.17)

1.6279

(1.06)

-0.0593

(-1.40) 0.8967 ∩ 13.72% 18.6%

JPN 15.2545

(1.20)

0.3491

(5.86)

0.0324

(0.95)

-1.5278

(-0.98)

0.0405

(0.86) 0.8533 U 18.87% 15.5%

LUX -8.7122

(-0.38)

0.0889

(2.42)

0.3483

(5.92)

1.5566

(0.46)

-0.0617

(-0.51) 0.6854 ∩ 12.62% 15.8%

NLD 20.2550

(1.40)

0.1390

(5.79)

0.1658

(6.68)

-1.2086

(-1.07)

0.0165

(0.76) 0.8537 U 36.71% 24.0%

NZL -51.407

(-1.63)

0.1036

(3.92)

0.1765

(2.21)

6.1754

(1.77)

-0.1819

(-1.90) 0.5986 ∩ 16.98% 18.3%

NOR 15.2046

(0.33)

0.1097

(5.61)

0.3257

(5.85)

-1.1891

(-0.26)

0.0237

(0.22) 0.6619 U 25.05% 20.4%

PRT 14.7010

(-0.19)

0.0228

(7.15)

0.0276

(3.11)

1.7922

(0.36)

0.0527

(-0.45) 0.7306 ∩ 17.01% 16.3%

ESP 4.8187

(1.54)

0.2949

(14.24)

0.1044

(3.60)

-0.3480

(-0.82)

0.0060

(0.43) 0.8587 U 29.23% 16.3%

SWE -10.631

(-0.35)

0.1358

(5.61)

0.1566

(3.67)

1.1206

(0.48)

-0.0259

(-0.58) 0.7732 ∩ 21.65% 26.9%

CHE 80.5783

(3.57)

-0.0009

(-0.01)

0.1605

(2.26)

-13.4694

(-3.03)

0.5610

(2.60) 0.5453 U 12.00% 10.9%

TUR -3.5814

(-0.71)

0.1988

(13.61)

0.0251

(1.39)

0.8744

(0.83)

-0.0396

(-0.75) 0.8307 ∩ 11.04% 10.1%

GBR -63.272

(-2.31)

0.1985

(8.73)

0.0274

(0.70)

6.8236

(2.55)

-0.1783

(-2.75) 0.8422 ∩ 19.14% 20.6%

USA 49.1177

(1.47)

0.2205

(11.79)

0.0658

(2.44)

-5.9348

(-1.40)

0.1817

(1.35) 0.8660 U 16.33% 16.5%

Notes: Figures in parentheses are t-statistics generated from HAC robust standard errors.

13

Of the 11 countries in the current study for which the government size-growth relationship

was U-shaped, 8 of them (AUT, BEL, IRL, JPN, NLD, NOR, ESP and CHE) had period

averages smaller than their respective estimated turning points of government size. The

relationship was strong for CHE but weak for the others. They may be said to be on the

declining part of the parabola where increases in government size would mitigate the

negative impact until government size reaches the estimated turning point. After that, further

increases in government size are expected to have positive impact on growth. The remaining

three countries, having period averages larger than their respective turning point values

(GER, GRC and USA), may be said to be on the rising segment of the parabola. This result

suggests those countries may increase government size without adversely affecting economic

growth. The relationship was strong for GER and GRC but weak for USA. In summary, the

findings about the growth-government size relationship for individual countries confirm a

robust BARS curve for CAN, DNK, FIN, NZL and GBR, a significant U-shaped relationship

for GER, GRC and CHE and insignificant relationship in the other countries.9

4.2 Quantile Regression Results

The results of the quantile regressions for selected quantiles are reported in Tables 4 and 5,

and the corresponding 95% confidence intervals of the GOVSIZE coefficients are plotted in

Figure 2. As the quantile regression results in Table 4 show, the impact of government size

on the per capita GDP growth rate is positive and insignificant below the 40th

quantile and

becomes negative from the 40th

through the 95th

quantile. For GDP growth rate, the impact is

also positive and insignificant below the 20th

quantile after which it becomes negative and

increasingly significant through the 95th

quantile (see Table 5).10

These results corroborate

the finding by Chen et al., (2011), who first reported such differential impact of government

size at different quantiles from a study of an unbalanced panel dataset for 24 OECD countries

for the 1971-2001 period. That study used GDP growth rate as the indicator for economic

growth and the ratio of government total expenditure to GDP as the proxy for government

size. The current study’s results add to the literature by showing that qualitatively similar

differential impacts are obtained for the alternative proxy for government size – government

final consumption as a ratio of GDP – when either economic growth indicator is used. The

implication from the analytical result from the current study is that when the per capita GDP

growth rate is less than 1.76% per annum or the aggregate GDP growth rate is less than 2.6%

per annum, [well targeted] stimulus can be growth-enhancing.

9 Re-estimation of the country models augmented with a trend variable did not appreciably change the overall

results. It was noticed, however, that the trend variable took a negative and significant coefficient (at the 5%

level or lower) in the cases of BEL, CAN, DNK, GER, ITA, LUX, NOR and USA, and at the 10% level for

IRL. In the cases of ESP and TUR the trend variable took a positive and significant coefficient at the 1% and

10% levels, respectively. The presence of the trend variable also forced the government size coefficients for

CAN to be insignificant and those for BEL and ESP to be monotonically decreasing and insignificant. 10

For the model with per capita GDP growth rate (GRYPC) as dependent variable, the sign of the coefficient is

positive up to the 14th

quantile and turns negative afterwards; the corresponding quantile for the model with

GDP growth rate (GRY) is the 36th

. Given the probability distributions of these variables in Figure 1, and

assuming normal distribution, the cut-off growth rates are 1.76% and 2.60%, respectively, for per capita GDP

growth rate and GDP growth rate.

14

Table 4

Quantile Regression Results – Dependent Variable is GRYPC

Variable Quantile, τ

0.10 0.20 0.30 0.40 0.50 0.75 0.95

Constant -1.0509***

(-2.67)

-0.4238

(-1.11)

-0.0759

(-0.31)

0.7419***

(2.80)

1.2977***

(5.80)

3.1892***

(6.88)

7.1354***

(11.35)

GRKPC 0.1721***

(18.01)

0.1703***

(20.93)

0.1684***

(27.08)

0.1651***

(21.99)

0.1706***

(20.79)

0.1616***

(16.20)

0.1122***

(8.87)

GREX 0.1237***

(7.17)

0.1302***

(7.51)

0.1365***

(9.54)

0.1429***

(12.70)

0.1403***

(16.61)

0.1506***

(9.22)

0.1680***

(6.43)

GOVSIZE 0.0123

(0.64)

0.0113

(0.62)

0.0119

(0.97)

-0.0146

(-1.17)

-0.0280***

(-2.58)

-0.0760***

(-3.40)

-0.1969***

(-7.58)

Statistics

Pseudo-R² 0.4533 0.4359 0.4205 0.4004 0.3832 0.3590 0.3805

Adj R² 0.4516 0.4340 0.4186 0.3985 0.3812 0.3570 0.3786

No. of Obs 936 936 936 936 936 936 936

Notes: Figures in parentheses are t-statistics which were obtained from the bootstrapped standard errors. Triple,

double and single asterisks (i.e., ***, ** and *) indicate statistical significance at the 1%, 5% and 10% levels,

respectively.

Table 5

Quantile Regression Results – Dependent Variable is GRY

Variable Quantile, τ

0.10 0.20 0.30 0.40 0.50 0.75 0.95

Constant -0.2750

(-0.97)

0.4816

(1.55)

1.0991***

(4.67)

1.5056

(6.08)

2.2413***

(7.12)

4.2814***

(9.96)

8.1099***

(13.91)

GRK 0.1685***

(15.95)

0.1641***

(21.09)

0.1609***

(21.83)

0.1605***

(21.41)

0.1699***

(18.18)

0.1594***

(19.93)

0.1183***

(6.73)

GRL 0.1327**

(2.24)

0.1846***

(3.66)

0.1949***

(4.39)

0.1938***

(5.12)

0.1289***

(3.16)

0.1279**

(2.10)

0.1387*

(1.83)

GREX 0.1351***

(6.66)

0.1262***

(8.27)

0.1254***

(9.64)

0.1275***

(12.13)

0.1225***

(12.01)

0.1398***

(8.93)

0.1764***

(5.96)

GOVSIZE 0.0002

(0.02)

-0.0114

(-0.83)

-0.0234**

(-2.36)

-0.0302***

(-2.71)

-0.0505***

(-3.60)

-0.1102***

(-5.70)

-0.2296***

(-9.14)

Statistics

Pseudo-R² 0.5093 0.4895 0.4707 0.4531 0.4352 0.3998 0.4169

Adj R² 0.5072 0.4873 0.4684 0.4508 0.4327 0.3972 0.4144

No. of Obs 936 936 936 936 936 936 936

Notes: Figures in parentheses are t-statistics which were obtained from the bootstrapped standard errors. Triple,

double and single asterisks (i.e., ***, ** and *) indicate statistical significance at the 1%, 5% and 10% levels,

respectively.

15

Figure 2

Plots of the Point Estimates and 95% Confidence Intervals of the Coefficient for the

Government Size Variable for all Quantiles for the Two Growth Models

4.3 Government Size Time Series Decomposition Results

Prior to the decomposition of the government size time series, their degrees of integration

were checked to ensure they were nonstationary and therefore decomposable. To check the

stationarity of the government size time series for each country, the Augmented Dickey

Fuller (ADF) and the Phillips Perron (PP) tests for unit root were implemented. The results of

the two tests were comparable and for brevity the ADF results are reported in Table 6. The

series for all the countries (and for the OECD group and the World11

) are integrated of order

one (i.e., I(1) or nonstationary) except for Austria, Germany and Iceland. For the latter two

countries the series are trend-stationary; only the series for Austria is stationary. Hence for

practical purposes it was assumed that all the series are nonstationary and therefore amenable

to decomposition.

Figure 3 shows plots of the observed government size time series and the HP trend and cycle

components for the sampled countries. In each illustration, the observed series is plotted with

diamond markers, the HP trend is plotted as a smooth solid line and the HP cycle is plotted as

a dashed line. It will be seen that the trend line tracks the actual series quite closely in all

countries. There are noticeable cycle components where the actual series diverge from the

trend. A visual examination suggests that the duration and dates of ‘government business

cycles’ are not the same for all the sampled countries. Since the objective of the study is not

to describe and date cycles, attention will be turned to the relative importance of the trend and

cycle components.

11

The results for the OECD group and for the world are included for comparison.

16

Table 6

Results of the ADF Unit Root Tests on Government Size

Country Constant Trend p-value Conclusion AUS Yes No 0.0135 I(1) AUT Yes No 0.0040 I(0) BEL Yes No 0.0995 I(1) CAN Yes No 0.1459 I(1) DNK Yes No 0.0698 I(1) FIN Yes Yes 0.0791 I(1) FRA Yes Yes 0.0265 I(1) GER Yes Yes 0.0043 I(0) GRC Yes Yes 0.0308 I(1) ISL Yes Yes 0.0031 I(0) IRL Yes No 0.6001 I(1) ITA Yes No 0.6703 I(1) JPN Yes No 0.9839 I(1) LUX Yes No 0.0095 I(1) NLD Yes No 0.6488 I(1) NZL Yes No 0.2566 I(1) NOR Yes No 0.0721 I(1) PRT Yes No 0.4587 I(1) ESP Yes No 0.2634 I(1) SWE Yes No 0.0138 I(1) CHE Yes No 0.0652 I(1) TUR Yes Yes 0.3015 I(1) GBR Yes No 0.1021 I(1) USA Yes No 0.2633 I(1) OECD Yes Yes 0.0232 I(1) World Yes No 0.3408 I(1)

The relative importance of the permanent and transitory components are determined by which

one accounts for more of the observed variance in the series (Murray and Papanyan, 2004).

The estimated variances of the HP trend and cycle components for the sampled countries are

reported in Table 7. For all countries except Australia, the trend variance is several times

bigger than the cycle variance and therefore the trend accounts for more of the variance in the

observed series. This suggests that permanent shocks are relatively more important than

transitory shocks. That is, most of the interesting dynamics of the government size series are

captured by the trend component and the cycle is largely noise. The permanency of the

changes is strongest in Portugal, followed by Spain, Japan and Iceland; it is weakest in

Luxembourg. It is only in Australia that the transitory changes trump the permanent changes.

17

Figure 3

Plots of Observed Government Size Series and Estimated HP Trend and Cycle Components

18

19

20

Table 7

Estimated Variances of the HP Trend and Cycle Components

Country Trend Variance Cycle Variance Country Trend Variance Cycle Variance

AUS 0.2563 0.2711 IRL 2.9297 0.7769

AUT 0.7254 0.1970 ISL 9.5515 0.3963

BEL 1.1087 0.3895 ITA 1.7351 0.2599

CAN 1.0000 0.6912 JPN 4.7805 0.1816

CHE 0.6359 0.1042 LUX 0.5614 0.5320

DNK 1.2487 0.7110 NLD 1.9550 0.4049

ESP 6.9425 0.2557 NOR 0.9900 0.8050

FIN 4.5203 0.8626 NZL 0.6832 0.4013

FRA 2.2196 0.2595 PRT 10.9204 0.3145

GBR 1.0420 0.5831 SWE 0.8759 0.7937

GER 0.5809 0.1967 TUR 4.1727 0.8018

GRC 3.0230 0.6844 USA 0.7762 0.1930

To explain why the changes in government size are predominantly of the permanent rather

than transitory nature, one needs to take a look at the extent of regulations within the

economy and the role of government in providing goods and services. Everywhere, the

increasing concerns about safety, financial crises and the environment have induced

corresponding expansions in the regulatory framework. The two important areas in which

government is a dominant player in service delivery are education and health. OECD reports

that, on the average, governments account for 70% and 85% of the final consumption

expenditures on health and education, respectively (OECD, 2011). With positive rates of

population growth and longer life expectancy, governments are faced with non-decreasing

demand for education and health services. For instance, schooling is compulsory until at least

the age of 15 and the majority of primary and secondary students are enrolled in government

run/financed institutions. The natural rate of population increase, no matter how low, implies

greater outlay to provide basic education to the young. Longer life expectancy and the

looming retirement of the ‘baby boomers’ means aged care will feature more prominently in

health services. This will be particularly acute in countries with universal public health

insurance.

The finding or conclusion from the HP decomposition that the changes in government size

are predominantly of the permanent rather than transitory nature warrants disambiguation

from Wagner’s law of increasing state activity and the Peacock-Wiseman hypothesis (in

public finance) both of which emphasise that public expenditure has a tendency to increase

over time and with economic development (Singh, 2008). Wagner’s law states that as nations

industrialise, the share of the public sector in the national economy grows continually for

reasons such as the state social functions that expand over time, administrative and protective

functions and welfare functions (Wagner, 1893, 1911). The Peacock-Wiseman hypothesis,

which is an elaboration of Wagner’s Law, states that over the years economic development

and income growth and the concomitant enlarged tax base results in an increase in

government revenue. That, in turn, leads to a boost in public expenditure because taxpayers

demand the provision of various services which the government cannot ignore (Peacock and

21

Wiseman, 1961). Wagner’s Law is validated when the long-run elasticity of government

expenditure with respect to GDP is larger than 1, and it is violated when the elasticity is less

than 1. In a review of the empirical assessments of Wagner’s Law, Durevall and Henrekson

(2011) report that around 65% of the studies find direct or indirect evidence in favour of the

concept while 35% provide no support. In a recent study on 23 OECD economies over the

period 1970-2006, Lamartina and Zaghini (2012) confirmed the existence of Wagner’s Law

but noted that the elasticity has declined over the years and also it is smaller for the relatively

richer countries. The latter observation is similar to the finding by Kuckuck (2012) who

tested the validity of Wagner’s Law for the UK, Denmark, Sweden, Finland and Italy over

the period 1850-2010 (more than 160 years). She found that the relationship between public

spending and economic growth has weakened with economic advancement and may have

reached its limit in recent decades.

The simultaneous consideration of the BARS curve and Wagner’s Law leads to what

Balatsky (2012) has referred to as ‘the paradox of wealth’. If, according to Wagner’s Law,

government size will continually increase with economic growth/development but

government size beyond a certain level has a dampening effect on the rate of growth, then it

is conceivable that if government size gets big enough it could stymie economic growth and

cause stagnation of wealth creation. Given the predominantly democratic institutions in

OECD countries, for instance, that level of displacement of the private sector by the public

sector is not likely to eventuate. In the event, Wagner’s Law acts as a serious limit to an

economy’s long-term growth and to eliminate the paradox of wealth it is necessary to

neutralise or violate that law. Findings of weakened relationship between government

spending and economic growth in the wealthiest countries and over time in growing

economies (e.g., Lamartina and Zaghini, 2012; Kuckuck, 2012) attest to the decline of

Wagner’s Law as part of economic evolution with its attendant growth. The consequence is

that the public sector’s share has ceased to increase monotonically and has changed over to a

fluctuating mode dynamically competing with the private sector. The finding that the shocks

to government size leave mainly permanent effects, rather than transitory effects, suggests

that deliberate policy choices have to be made to reduce government size. And here, the

education and health sectors offer perhaps the best leverage. In health, reforms that can

decrease the role of government include co-payments by households for some services like

doctor visits, privatisation of state-owned hospitals and the liberalisation of the health

insurance market. In the expenditure on education, OECD (2011) reports that most of the

differences between countries lie in the extent to which the governments finance pre-primary

and tertiary education. Korea, for instance, has a relatively higher enrolment rate in private

educational institutions at the pre-primary and university levels as well as a higher use of

private tutoring. Thus, Korea’s public expenditure on education as a percentage of GDP is

one of the lowest in the OECD.

5. SUMMARY AND CONCLUSION

The concurrence of large government size, slow growth and persistent unemployment in the

wake of the 2008/09 global financial crisis and the widespread expectation for governments

to downsize has renewed interest in the relationship between government size and economic

growth. The study analysed four aspects of the fluctuations in government size in 24

countries that have been members of the OECD since 1973; the most up to date data were

used. Firstly, the nature of the relationship between government size and economic growth

22

was investigated. A check with panel fixed effects model found that economic growth is

significantly negatively related to government size. Secondly, the introduction of the

quadratic term of the government size variable revealed that the relationship has an inverse-U

shape for the whole sample which provided a basis for the estimation of a growth-maximising

size of government. Checks of the individual sampled countries’ data yielded almost an equal

mixture of countries with U-shaped and inverse-U shaped relationships. This suggests that

increases in government size are not uniformly detrimental to economic growth for all

countries. Thirdly, a further check with quantile regression models for the whole sample

revealed that the impact of government size on economic growth is positive and insignificant

at low rates of economic growth. This impact decreases as economic growth increases and

eventually turns negative and significant at relatively high rates of economic growth. This

finding corroborates the finding by the first study to apply quantile regression to the

government size-economic growth relationship (Chen et al., 2011) that analysed an

unbalanced panel dataset for 24 OECD countries for the 1971-2001 period.

Fourthly, the study decomposed the government size time series of the sampled countries into

the permanent/trend and transitory/cycle components. This was done in an effort to

characterise whether the shocks to government size are predominantly of the permanent type

or of the transitory type. The trend component captures shocks that have a permanent effect

on the level of the variable, and the cycle component captures shocks that only have a

temporary effect on the level of the variable. Permanent shocks might need deliberate counter

measures to ameliorate perceived negative effects; nothing deliberate has to be done in the

case of transitory shocks since the economy may self-correct in due course. The Hodrick-

Prescott (HP) decomposition technique was employed. For the whole sample, and for 23 out

of the 24 countries, it was estimated that the shocks to government size were more of the

permanent type than the transitory type. It was for Australia only that the shocks were

deemed to be more of the transitory nature.

23

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27

APPENDICES

Appendix 1: The Raw Data on Government Size

Year AUS AUT BEL CAN DNK FIN FRA GER GRC ISL IRL ITA JPN LUX NLD NZL NOR PRT ESP SWE CHE TUR GBR USA Avge

1973 14.4 15.2 18.5 20.0 21.6 15.6 17.8 18.5 10.2 15.3 17.4 16.6 11.9 11.5 20.6 15.0 17.5 11.2 10.2 23.0 8.6 9.1 18.8 16.8 15.6

1974 16.7 15.9 18.9 20.3 23.6 15.8 18.3 20.0 12.6 17.0 18.9 15.8 13.1 11.7 21.5 16.9 17.7 12.3 10.6 23.5 8.9 8.2 20.5 17.3 16.5

1975 17.8 17.4 21.1 21.8 24.8 17.5 19.8 21.1 13.7 17.4 20.6 16.2 14.4 15.3 22.9 18.1 18.7 13.1 11.2 24.1 9.7 9.2 22.4 17.9 17.8

1976 17.4 17.8 21.3 21.8 24.3 18.6 20.1 20.6 13.4 16.6 19.9 15.7 14.1 15.1 22.8 16.9 19.4 12.0 12.1 25.2 10.1 10.1 22.2 17.3 17.7

1977 17.8 17.6 21.8 22.3 24.2 19.1 20.4 20.5 14.2 16.7 19.0 16.0 14.1 16.3 23.5 18.1 19.6 12.2 12.3 27.9 10.0 11.2 20.8 17.0 18.0

1978 17.3 18.3 22.6 21.8 24.8 18.7 20.8 20.7 14.0 17.7 19.3 16.5 13.9 16.0 23.9 19.4 20.1 12.1 12.8 28.3 9.9 9.8 20.5 16.4 18.2

1979 17.1 18.1 22.9 21.1 25.5 18.3 20.8 20.6 14.4 18.3 20.2 16.6 13.9 16.4 24.7 19.2 19.6 12.1 13.3 28.7 9.9 9.3 20.2 16.2 18.2

1980 17.7 18.2 22.8 21.3 27.1 18.4 21.4 21.1 14.4 17.6 22.1 16.8 14.1 17.1 24.5 20.2 19.2 12.7 14.0 29.3 9.8 6.8 21.7 16.8 18.5

1981 17.6 18.6 24.0 21.2 27.9 19.0 22.2 21.6 15.8 17.9 22.1 18.2 14.1 17.8 24.5 19.8 19.4 13.1 14.9 29.6 9.9 8.8 22.3 16.6 19.0

1982 18.6 19.0 23.7 22.9 28.4 19.2 22.7 21.3 16.0 18.9 21.8 18.3 14.2 16.8 25.0 19.7 19.7 13.0 15.1 29.5 10.2 7.6 22.2 17.6 19.2

1983 18.2 19.0 23.4 22.7 27.6 19.6 22.8 21.0 16.6 18.9 21.4 18.7 14.5 16.1 24.9 18.8 19.7 13.2 15.6 28.8 10.5 8.4 22.0 17.5 19.2

1984 18.8 19.0 23.3 21.8 25.7 19.6 23.0 20.8 17.1 17.4 20.9 18.5 14.3 15.7 23.4 18.1 18.8 13.1 15.3 27.8 10.3 7.2 21.7 17.1 18.7

1985 18.9 19.2 22.8 21.8 25.3 20.6 23.1 20.8 17.8 18.3 20.8 18.6 13.9 16.1 23.7 18.3 18.5 13.5 15.6 27.5 10.4 7.2 20.9 17.5 18.8

1986 18.9 19.5 22.7 21.7 24.2 20.8 22.8 20.6 16.8 18.8 21.1 18.3 13.9 15.8 23.7 18.5 19.6 13.4 15.4 27.0 10.5 7.3 20.9 17.8 18.7

1987 17.9 19.4 22.4 21.1 25.3 21.2 22.6 20.9 17.2 19.6 20.1 19.0 13.9 16.7 24.6 18.1 20.7 13.2 15.9 26.2 10.5 6.3 20.3 17.8 18.8

1988 17.3 19.2 21.0 20.9 25.8 20.5 22.2 20.6 15.1 20.7 18.7 19.4 13.5 15.8 24.0 18.0 20.9 13.7 15.7 25.6 10.7 6.1 19.6 17.3 18.4

1989 17.1 18.8 19.9 21.1 25.5 20.2 21.7 19.6 16.0 20.1 17.5 19.2 13.4 15.4 23.2 18.1 20.7 14.4 16.3 25.8 11.1 7.5 19.4 16.9 18.3

1990 18.0 18.6 19.7 22.3 25.1 21.8 21.7 19.3 16.1 19.9 17.9 20.0 13.3 15.8 23.0 18.8 21.2 15.2 16.7 26.9 11.3 8.8 19.7 17.0 18.7

1991 18.9 18.8 20.6 23.7 25.3 24.9 22.2 19.1 15.2 20.6 18.9 20.1 13.4 15.3 23.2 19.2 21.8 16.9 17.4 27.6 11.7 10.0 20.7 17.2 19.3

1992 18.8 19.1 20.9 24.1 25.3 25.4 22.8 19.6 14.7 21.2 19.3 20.0 13.8 15.9 23.8 19.3 22.7 16.9 18.3 28.7 12.2 10.4 21.2 16.8 19.6

1993 18.2 20.0 21.2 23.5 26.4 24.2 24.0 19.6 15.3 21.7 19.1 19.9 14.3 15.8 24.1 18.1 22.5 17.5 18.8 28.8 12.0 10.5 20.4 16.2 19.7

1994 17.9 20.1 21.2 22.3 25.5 23.5 23.7 19.5 14.7 21.6 18.9 19.2 14.7 15.4 23.9 17.2 22.2 17.6 18.2 27.9 11.9 9.4 20.0 15.7 19.2

1995 17.8 20.3 21.4 21.3 25.2 22.7 23.6 19.4 16.4 22.1 17.7 17.8 15.2 15.9 23.8 17.2 21.6 17.5 18.1 26.6 11.8 8.7 19.5 15.4 19.0

1996 17.5 20.2 21.8 20.5 25.4 23.2 23.9 19.7 15.5 21.9 17.1 18.1 15.4 16.4 22.8 17.0 20.9 17.7 18.0 27.3 11.9 9.3 19.0 15.0 19.0

28

1997 17.5 19.3 21.3 19.5 25.0 22.5 23.9 19.3 16.2 21.7 16.3 18.2 15.4 16.7 22.3 17.8 20.6 17.7 17.5 26.7 11.6 9.9 18.0 14.6 18.7

1998 17.8 19.2 21.2 19.6 25.6 21.8 23.1 19.1 16.4 22.2 15.5 18.0 15.9 15.8 22.2 17.7 22.0 17.9 17.3 26.8 11.3 10.3 17.8 14.3 18.7

1999 17.6 19.6 21.4 18.9 25.7 21.4 23.2 19.2 16.5 22.9 14.9 18.1 16.5 15.5 22.2 18.0 21.6 18.1 17.2 26.7 11.1 12.2 18.3 14.3 18.8

2000 17.6 19.0 21.3 18.6 25.1 20.6 22.9 19.0 18.9 23.4 14.7 18.3 16.9 15.1 22.0 17.2 19.3 19.0 17.1 25.8 11.1 11.7 18.7 14.3 18.7

2001 17.4 18.7 21.7 19.1 25.7 20.7 22.8 19.0 18.4 23.6 15.5 18.8 17.7 16.1 22.6 17.2 20.6 19.4 17.0 26.3 11.6 12.4 19.1 14.8 19.0

2002 17.5 18.4 22.5 19.5 26.2 21.4 23.5 19.2 19.4 25.4 16.0 19.0 18.3 16.5 23.7 17.0 22.1 19.7 17.1 27.0 11.8 12.7 19.9 15.4 19.6

2003 17.4 18.7 22.9 19.7 26.5 22.1 23.8 19.3 18.1 26.0 16.1 19.5 18.3 16.4 24.5 17.2 22.5 20.0 17.3 27.3 12.0 12.2 20.5 15.8 19.8

2004 17.4 18.4 22.5 19.2 26.5 22.2 23.8 18.9 18.3 25.0 16.4 19.7 18.2 16.9 24.2 17.5 21.2 20.3 17.8 26.5 11.8 11.9 20.9 15.8 19.6

2005 17.3 18.4 22.7 18.9 26.0 22.5 23.8 18.8 18.1 24.6 16.3 20.1 18.4 16.5 23.7 18.0 19.7 21.1 18.0 26.2 11.6 11.8 21.2 15.8 19.6

2006 17.2 18.3 22.4 19.1 25.9 22.2 23.5 18.4 17.0 24.4 16.5 20.0 18.2 15.4 25.1 18.5 18.9 20.5 18.0 26.0 11.1 12.3 21.4 15.8 19.4

2007 17.1 18.0 22.2 19.2 26.0 21.5 23.1 17.9 17.8 24.2 17.2 19.5 18.1 14.8 25.2 18.6 19.3 19.8 18.3 25.5 10.7 12.8 20.9 15.9 19.3

2008 17.6 18.7 23.1 19.7 26.5 22.5 23.3 18.3 18.1 24.8 19.2 20.0 18.6 14.8 25.7 20.2 19.1 20.1 19.5 26.1 10.4 12.8 21.9 16.9 19.9

2009 18.1 19.8 24.7 22.1 29.8 25.2 24.8 20.0 20.4 26.5 20.4 21.4 19.9 16.9 28.6 20.3 22.5 22.1 21.3 27.7 11.2 14.7 23.4 17.9 21.6

2010 17.9 19.4 24.3 21.8 29.1 24.7 24.9 19.5 18.2 25.9 19.2 21.1 19.8 16.6 28.4 20.1 22.0 21.6 21.4 26.7 11.0 14.3 22.8 17.9 21.2

2011 17.7 18.8 24.4 21.4 28.6 24.3 24.5 19.3 17.5 25.3 18.4 20.5 20.6 16.5 27.9 20.2 21.5 20.1 20.9 26.4 11.1 13.9 22.4 17.3 20.8

Year AUS AUT BEL CAN DNK FIN FRA GER GRC ISL IRL ITA JPN LUX NLD NZL NOR PRT ESP SWE CHE TUR GBR USA Avge

Avge 17.7 18.7 22.0 21.0 25.9 21.1 22.5 19.8 16.2 21.1 18.5 18.6 15.5 15.8 24.0 18.3 20.4 16.3 16.3 26.9 10.9 10.1 20.6 16.5 18.9

Sources: World Bank and OECD databases.

29

Appendix 2

Country-wise Correlations of Government Size

AUS AUT BEL CAN DNK FIN FRA GER GRC ISL IRL ITA JPN LUX NLD NZL NOR PRT ESP SWE CHE TUR GBR USA

AUS 1.00

AUT 0.66 1.00

BEL 0.42 0.40 1.00

CAN 0.55 0.20 0.06 1.00

DNK 0.40 0.48 0.80 0.15 1.00

FIN 0.47 0.72 0.29 0.15 0.49 1.00

FRA 0.49 0.82 0.60 -0.09 0.68 0.84 1.00

GER 0.37 0.06 0.28 0.54 0.13 -0.43 -0.28 1.00

GRC 0.40 0.55 0.61 -0.32 0.62 0.58 0.81 -0.27 1.00

ISL 0.08 0.44 0.37 -0.42 0.54 0.75 0.80 -0.65 0.79 1.00

IRL 0.39 -0.01 0.36 0.71 0.24 -0.25 -0.22 0.79 -0.27 -0.56 1.00

ITA 0.33 0.52 0.36 0.04 0.61 0.83 0.78 -0.43 0.69 0.77 -0.18 1.00

JPN 0.01 0.24 0.57 -0.47 0.61 0.54 0.68 -0.52 0.76 0.90 -0.42 0.59 1.00

LUX 0.55 0.64 0.71 0.15 0.66 0.43 0.63 0.34 0.53 0.31 0.19 0.34 0.31 1.00

NRL 0.31 0.35 0.80 0.24 0.84 0.50 0.56 0.07 0.51 0.50 0.35 0.60 0.61 0.50 1.00

NZL 0.51 0.29 0.65 0.40 0.71 0.32 0.33 0.28 0.29 0.16 0.53 0.39 0.25 0.52 0.75 1.00

NOR 0.35 0.66 0.14 0.22 0.42 0.79 0.63 -0.22 0.38 0.60 -0.28 0.58 0.34 0.46 0.32 0.15 1.00

PRT 0.07 0.40 0.30 -0.41 0.48 0.77 0.77 -0.72 0.73 0.97 -0.59 0.74 0.88 0.24 0.42 0.11 0.55 1.00

ESP 0.36 0.71 0.44 -0.07 0.65 0.92 0.93 -0.49 0.75 0.87 -0.30 0.89 0.72 0.44 0.60 0.37 0.68 0.86 1.00

SWE 0.61 0.54 0.55 0.49 0.52 0.29 0.40 0.44 0.19 -0.02 0.43 0.18 -0.06 0.72 0.35 0.56 0.36 -0.05 0.24 1.00

CHE 0.38 0.71 0.09 -0.03 0.30 0.85 0.78 -0.45 0.56 0.72 -0.50 0.66 0.44 0.45 0.16 -0.02 0.82 0.72 0.78 0.27 1.00

TUR -0.13 0.03 0.32 -0.34 0.39 0.50 0.44 -0.63 0.50 0.76 -0.49 0.45 0.85 0.09 0.43 0.15 0.36 0.81 0.57 -0.10 0.38 1.00

GBR 0.33 -0.06 0.63 0.44 0.57 0.06 0.09 0.38 0.15 0.01 0.69 0.21 0.25 0.25 0.71 0.63 -0.13 0.00 0.09 0.24 -0.24 0.17 1.00

USA 0.24 -0.21 0.21 0.64 0.17 -0.16 -0.27 0.53 -0.19 -0.38 0.80 0.04 -0.25 -0.05 0.42 0.43 -0.24 -0.43 -0.22 0.02 -0.48 -0.30 0.73 1.00

30